Unlike the Gaussian channel, the wireless channel suffers from multi-path fading. In such fading environments, reliable communication is made possible only through the use of diversity techniques in which the receiver is afforded multiple replicas of the transmitted signal under varying channel conditions. Recently, information theoretic studies have shown that spatial diversity provided by multiple transmit and/or receive antennas allows for a significant increase in the capacity of wireless communication systems operated in a Rayleigh fading environment. Following this research, two approaches for exploiting this spatial diversity have been proposed.
In accordance with one approach, channel coding is performed across the spatial dimension, as well as time, to benefit from the spatial diversity provided by using multiple transmit antennas. Accordingly, the term “space-time codes” is used in connection with this coding scheme. One potential drawback of this scheme is that the complexity of the maximum likelihood (ML) decoder is exponential in the number of transmit antennas.
A second approach relies on complex signal processing techniques at the receiver to achieve performance asymptotically close to the outage capacity. In this approach, no effort is made to optimize the channel coding scheme. Conventional single-dimensional channel codes are used to minimize complexity. This approach is referred to as the layered space-time (LST) architecture. The LST architecture involves formulating the problem as a multi-user detection problem at the receiver and, hence, capitalizing on existing multi-user detection techniques in the receiver design. A proposed algorithm is based on a combination of decision feedback interference cancellation and zero-forcing interference avoidance. One drawback of the LST architecture is that the number of receive antennas must be at least equal to the number of transmit antennas. The LST signal processing does not gain the maximum diversity advantage that space-time coding offers. At low signal-to-noise ratios, this approach may suffer from error propagation resulting from the decision feedback cancellation.